1. Field of Invention
The invention relates generally to methods and systems for detection, localization, and characterization of defects in a structure, and more particularly, to a method and system for passive detection, localization, and characterization of mechanical wave sources using ultrasonic guided waves.
2. Discussion of Related Art
Acoustic emission (AE) technology has been used for over 50 years to detect and track the initiation or growth of material defects. At the most fundamental level, acoustic emission systems may use permanently attached sensors to provide continuous monitoring of a structure in an attempt to record and analyze transient mechanical waves associated with, for example, the initiation and growth of cracks, dislocation movements, corrosion effects, metal phase transformations, and external impacts. By combining information about the frequency, magnitude, and location of one or more source events with reliable prognosis tools, repairs and replacements can be performed on an as-needed basis, simultaneously reducing maintenance costs and potentially avoiding unforeseen catastrophic failures.
The fundamental challenges associated with such monitoring fall into the general categories of detection, localization and characterization. The detection problem is largely associated with discriminating acoustic or elastic waves emanating from sources of interest from acoustic or elastic waves originating from benign environmental or operational sources. In general, the detection problem may be addressed by, for example, intelligent sensor design, parametric analysis, signal-based analysis, and artificial intelligence methods. In some applications, detection alone may provide sufficient information since the frequency and magnitude of source events has been repeatedly shown to be a strong indicator of impending material failure. In many cases, however, the location of the source event is critical to effective detection since the source location can be used to separate benign events from events of interest.
The source localization challenge may generally be addressed by analyzing the relative amplitude and time-difference-of-arrival (TDOA) of waves simultaneously recorded from multiple sensors. Linear and zonal location techniques may constrain the source location either to a line between two sensors (linear) or a spatial area that is based on sensor placement (zonal). Triangulation methods, for example, may use three or more sensors to triangulate the source using TDOA information. In all cases, it may be the direct-path TDOA information that is combined with assumed or measured propagation velocity profiles and geometric configuration (planar, cylindrical, spherical, etc.) to determine the source location.
While direct path techniques work well for simple structures, such as pressure vessels, this simplified approach to source localization becomes problematic in complex structures, particularly for AE systems that use dispersive guided waves to monitor large, complex, plate-like structures. Structural complexity can result from any number of geometric features, including cut-outs, stiffeners, rivets, thickness variations, and anisotropic materials (e.g. carbon-fiber reinforced plastics). When a structure is complex, guided waves travel in very complex paths via multiple modes and there is often not a direct path between a potential source location and sensors.
Another problem with triangulation TDOA methods is that they require propagation velocity profiles for accurate and reliable source localization. As such, calibration is often performed for structures in which the propagation velocity either varies with location or is unknown due to material or process variability. Calibration data, however, is often obtained through tedious and error-prone manual measurements of received waveforms using, for example, pencil break, glass capillary break, or active transducer sources, which can be very costly and limit localization performance.
Characterization of sources is often problematic, and is usually achieved by ad hoc methods that attempt to correlate measured signal characteristics (e.g., duration, center frequency, etc.) with different damage mechanisms. An alternative approach is based upon a more fundamental understanding of acoustic emission sources, which is related to the actual source mechanism. The source mechanism can be modeled by a source-time function and a directivity pattern, and if these quantities can be estimated, the source can be characterized based upon its physical attributes.
Localization challenges that arise from structural complexities can be mitigated with high sensor density, but this approach is usually not economically viable because of the increased cost and weight. Another alternative to help address complexity is the use of artificial intelligence techniques, such as neural networks. These methods are problematic from several different perspectives. First, since performance is sensitive to training and calibration data, it is difficult to quantify expected performance. Second, there is a discomforting lack of insight into the trained systems since artificial intelligence methods generally do not result in a set of intuitive rules that can be explained, justified, or even documented. Finally, there is a relatively high level of operator training required in order to effectively and reliably train and interpret artificial intelligence systems, which increases the cost and complexity of effective implementation.